Low dimensional successive projection pursuit cluster (LDSPPC) model comprehensive evaluation method, device and application
The invention discloses a low dimensional successive projection pursuit cluster (LDSPPC) model comprehensive evaluation method, device and application. The method comprises a step of carrying out normalized preprocessing on sample data of multiple candidate objects, and constructing a one-dimensiona...
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| creator | TANG JUN SI WEN FENG GUOZHEN LOU WENGAO YU XIAOHONG |
| description | The invention discloses a low dimensional successive projection pursuit cluster (LDSPPC) model comprehensive evaluation method, device and application. The method comprises a step of carrying out normalized preprocessing on sample data of multiple candidate objects, and constructing a one-dimensional projection pursuit cluster model with 2 to 4 mutually orthogonal projection vectors for the candidate objects, and a step of carrying out projection pursuit cluster model vector synthesis of all dimensions of the multiple candidate objects to form a comprehensive projection pursuit cluster model, and obtaining an evaluation index importance ranking list and a candidate object quality ranking list. The swarm search intelligent algorithm of the invention has the characteristics of a fast convergence speed, convergence to a globally optimal solution and high reliability, the vector synthesis of multiple successive projection pursuit vectors is carried out, the quality of the candidate objects can be quickly evaluate |
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The method comprises a step of carrying out normalized preprocessing on sample data of multiple candidate objects, and constructing a one-dimensional projection pursuit cluster model with 2 to 4 mutually orthogonal projection vectors for the candidate objects, and a step of carrying out projection pursuit cluster model vector synthesis of all dimensions of the multiple candidate objects to form a comprehensive projection pursuit cluster model, and obtaining an evaluation index importance ranking list and a candidate object quality ranking list. 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The method comprises a step of carrying out normalized preprocessing on sample data of multiple candidate objects, and constructing a one-dimensional projection pursuit cluster model with 2 to 4 mutually orthogonal projection vectors for the candidate objects, and a step of carrying out projection pursuit cluster model vector synthesis of all dimensions of the multiple candidate objects to form a comprehensive projection pursuit cluster model, and obtaining an evaluation index importance ranking list and a candidate object quality ranking list. The swarm search intelligent algorithm of the invention has the characteristics of a fast convergence speed, convergence to a globally optimal solution and high reliability, the vector synthesis of multiple successive projection pursuit vectors is carried out, the quality of the candidate objects can be quickly evaluate</abstract><oa>free_for_read</oa></addata></record> |
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| subjects | CALCULATING COMPUTING COUNTING DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES HANDLING RECORD CARRIERS PHYSICS PRESENTATION OF DATA RECOGNITION OF DATA RECORD CARRIERS SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR |
| title | Low dimensional successive projection pursuit cluster (LDSPPC) model comprehensive evaluation method, device and application |
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